Matching and Mismatching Social Contexts

  • Bruce EdmondsEmail author
Part of the Studies in the Philosophy of Sociality book series (SIPS, volume 3)


Social Contexts are specific types of recognised social situation for which specific norms, habits, rules, etc. are developed over time. The unconscious and embedded nature of these make them difficult to change – becoming deeply entrenched over time. How cultures relate can be effected, in detail, on whether contexts in one culture are identified with ones in the other, carrying along with these engrained assumptions and expectations. This chapter explores the implications of social context to the problem of integrating cultures, examining each of the possible subcases in turn. It concludes by noting that how social contexts in different cultures map onto each other (or not) matters greatly in terms of both the outcomes of meeting cultures and the steps that might be taken to facilitate their integration. However the possible interactions are complex and dynamic, so the chapter ends by considering simulations that might start to explore such complexities and outlining some ways to approach this.


Social Context Human Cognition Situational Context Linguistic Context Social Embedding 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The research was done under grants GR/T11760/01 and EP/H02171X/1, both from the EPSRC. Its support and that of the MMUBS is gratefully acknowledged.


  1. Alam, S. J., Geller, A., Meyer, R. and Werth, B. (2010). Modelling Contextualized Reasoning in Complex Societies with “Endorsements”. Journal of Artificial Societies and Social Simulation 13(4)6 <>
  2. Andrighetto, G., Campennì, M., Conte, R., and Cecconi, F. (2008). Conformity in Multiple Contexts: Imitation Vs Norm Recognition,. In World Congress on Social Simulation 2008 (WCSS-08) George Mason University, Fairfax, USA.Google Scholar
  3. Axtell, R. L. and J. M. Epstein (1996). Growing Artificial Societies – Social Science From the Bottom Up. Brookings Institution Press and MIT Press.Google Scholar
  4. Axtell, R. L. and J. M. Epstein (1994). Agent-based Modelling: Understanding our Creations. The Bulletin of the Santa Fe Institute 9: 28–32.Google Scholar
  5. Barwise, J. and Perry, J. (1983). Situations and Attitudes. Cambridge: MIT Press.Google Scholar
  6. Conte, R. et al. (2010) EMIL-T: The final report of the EU 6FP EMIL Project.
  7. Cohen. P. R. and Grinberg, M. R. (1983) A Theory of Heuristic Reasoning About Uncertainty, AI Magazine, 4(2)
  8. Deacon, T.W. (1998) Symbolic Species: The Co-Evolution of Language and the Brain. Norton & Co.Google Scholar
  9. Edmonds, B. (1998). Modelling Socially Intelligent Agents. Applied Artificial Intelligence, 12, 677–699.CrossRefGoogle Scholar
  10. Edmonds, B. (1999a) The Pragmatic Roots of Context. CONTEXT’99, Trento, Italy, September 1999. Lecture Notes in Artificial Intelligence, 1688:119–132.Google Scholar
  11. Edmonds, B. (1999b). Capturing Social Embeddedness: a Constructivist Approach. Adaptive Behavior, 7:323–348.CrossRefGoogle Scholar
  12. Edmonds, B. (2001) Learning Appropriate Contexts. In: Akman, V. et. al (eds.) Modelling and Using Context – CONTEXT 2001, Dundee, July, 2001. Lecture Notes in Artificial Intelligence, 2116:143–155.Google Scholar
  13. Edmonds, B. (2009) The Nature of Noise. In Squazzoni, F. (Ed.) Epistemological Aspects of Computer Simulation in the Social Sciences. Lecture Notes in Artificial Intelligence, 5466:169–182.Google Scholar
  14. Edmonds, B. (2010a) Bootstrapping Knowledge About Social Phenomena Using Simulation Models. Journal of Artificial Societies and Social Simulation 13(1)8. (
  15. Edmonds, B. (2010b) Agent-Based Social Simulation and its necessity for understanding socially embedded phenomena. CPM Report No.: 10–205, MMU. (
  16. Edmonds, B. (2012) Complexity and Context-dependency. Foundations of Science. Online First. DOI 10.1007/s10699-012-9303-xGoogle Scholar
  17. Edmonds, B. and Moss, S. (2001) The Importance of Representing Cognitive Processes in Multi-Agent Models, Invited paper at Artificial Neural Networks – ICANN’2001, Aug 21–25 2001, Vienna, Austria. Published in: Dorffner, G., Bischof, H. and Hornik, K. (eds.), Lecture Notes in Computer Science, 2130:759–766.Google Scholar
  18. Edmonds, B. and Moss, S. (2005) From KISS to KIDS – an ‘anti-simplistic’ modelling approach. In P. Davidsson et al. (Eds.): Multi Agent Based Simulation 2004. Springer, Lecture Notes in Artificial Intelligence, 3415:130–144.Google Scholar
  19. Edmonds, B. & Norling, E. (2007) Integrating Learning and Inference in Multi-Agent Systems Using Cognitive Context. In Antunes, L. and Takadama, K. (Eds.) Multi-Agent-Based Simulation VII, Lecture Notes in Artificial Intelligence 4442:142–155.Google Scholar
  20. Gärdenfors, P., The pragmatic role of modality in natural language. in 20th Wittgenstein Symposium, (Kirchberg am Weshel, Lower Austria, 1997), Wittgenstein Society.Google Scholar
  21. Gilbert, N. (2006) When Does Social Simulation Need Cognitive Models? In Sun, R. (ed.) Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, 428–432.Google Scholar
  22. Granovetter, M. (1985) Economic Action and Social Structure: the Problem of Embeddedness., American Journal of Sociology, 91:481–93CrossRefGoogle Scholar
  23. Greiner, R., Darken, C. & Santoso, N.I. (2001) Efficient reasoning. ACM Comp. Surveys, 33(1):1–30.CrossRefGoogle Scholar
  24. Hayes, P. (1995). Contexts in Context. Context in Knowledge Representation and Natural Language, AAAI Fall Symposium, November 1997, MIT, Cambridge.Google Scholar
  25. Kokinov, B. and Grinberg, M. (2001) Simulating Context Effects in Problem Solving with AMBR. in Akman, V., Bouquet, P., Thomason, R. and Young, R.A. eds. Modelling and Using Context, Springer-Verlag, 2116:221–234.Google Scholar
  26. Kummer, H., Daston, L., Gigerenzer, G., & Silk, J. (1997). The social intelligence hypothesis. In P. Weingart, S. D. Mitchell, P. J. Richerson, & S. Maasen (Eds.), Human by nature. Between biology and the social sciences (pp. 157–179). Mahwah, NJ: Erlbaum.Google Scholar
  27. Schlosser, A., Voss, M. and Brückner, L. (2005). On the Simulation of Global Reputation Systems. Journal of Artificial Societies and Social Simulation 9(1)4 <>.
  28. Simon, H. (1976). Administrative Behavior (3rd ed.). New York: The Free PressGoogle Scholar
  29. Tykhonov, D, Jonker, C, Meijer, S and Verwaart, T (2008), Agent-Based Simulation of the Trust and Tracing Game for Supply Chains and Networks. Journal of Artificial Societies and Social Simulation 11(3) 1
  30. Ye, M. & K. M. Carley (1995), Radar-Soar: Towards An Artificial Organization Composed of Intelligent Agents, Journal of Mathematical Sociology 20(2–3): 219–246.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Centre for Policy ModellingManchester Metropolitan UniversityManchesterUK

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